Deep Convolutional Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve problems of character recognition, but their use has become as widespread as it is now thanks to recent researches. These CNNs have won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolutional neural network became a very useful tool in the field of the machine learning.
Recently, the CNNs have been widely used in autonomous driving. The CNNs can perform various functions to support the autonomous driving, such as semantic segmentation, object detection, etc., for autonomous vehicles, which are learned by using tens of thousands of training data sets, and then installed on the autonomous vehicles.
However, it is difficult to determine whether or not the CNN operates in a stable condition while the CNN is operating a vehicle. It is very important to be able to know whether the CNN is operating in a normal condition, because it is necessary to notify a user to operate the vehicle if the CNN is operating in an unstable condition, and record any problems in order to improve the CNN in the future.
As a conventional technique, there is a method for presenting a basis for an object detection result of the CNN having a configuration satisfying a specific condition. However, the specific condition is very limited, and the conventional technique has a disadvantage that it is difficult to apply to general cases. That is, the CNN must be configured to use a Class Activation Map. Therefore, there is no prior art that can detect whether the CNN of the general configuration is stable or not.